Dataiku vs. Sisense for Cloud Data Teams

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

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Sisense for Cloud Data Teams is suited so well for our project that works with lots of data and needs some ways to share data internally or externally with our clients. It's very easy to pull out the data from the sense in best and in a suitable format and moreover a huge number of options are available there to represent the data. All features of this Sisense for cloud data teams software can be taken advantage of if you have a team who are well versed in data analytics, data management, and programming.

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• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

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• Rapid deployment of polished T-SQL-based data visualization charts and dashboards. Periscope supports a variety of database technologies, and allows users to write custom queries to display data.
• Included caching to reduce server load.
• Outstanding customer service/support, with expert advice as needed.
• Constant updates and new features.
• Built-in SQL formatters take the pain out of manipulating date/time objects.

• End product deployment.

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• Include more appropriate updates in their monthly newsletter as opposed to the general overview that doesn't always have the most pertinent information at the forefront.
• There are bugs at times when certain dashboards are down, though not in an overwhelming way.
• If you do not tag something it shows up in multiple views and may be accessible to people that it should not be, though that is a user error it may make sense to prompt for a tag and security setting.

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As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

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My company has had Periscope for various use cases in the past and I think that this program opens up complex data reports to non-technical people in a really accessible way (even though the learning curve is a big one). We are now integrating Sisense for Cloud Data Teams at a larger level both for internal data exploration and for customer facing dashboards and reports.

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The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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The user interface is just amazing and it's really very easy to use and navigate, even for a newbie. Mostly it provides us the accurate data.

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Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

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Google Analytics works well but it does not have all of the bells and whistles that Periscope Data offers. Google Analytics is best used in a Google environment but if you are using other tools and programs outside of the Google universe, then Periscope Data is a much better option.

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• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

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• Very positive - has allowed us to glean insights quickly and in real time.
• Definitely helpful for evaluating performance of accounts and employees.
• Versatile - we even use it with recruiting metrics, our software team uses it to test feature deployment.

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